为提高终端区时空资源利用率,增强空中交通运行效率,研究了复杂终端区进场交通流优化排序问题。通过深入剖析终端区进场定位点、航路航线、多跑道系统等资源运行特性,综合考虑尾流间隔、移交间隔、多跑道运行间隔等各类约束限制,以及最...为提高终端区时空资源利用率,增强空中交通运行效率,研究了复杂终端区进场交通流优化排序问题。通过深入剖析终端区进场定位点、航路航线、多跑道系统等资源运行特性,综合考虑尾流间隔、移交间隔、多跑道运行间隔等各类约束限制,以及最小化航班延误时间、最大化跑道运行容量、最小化终端区飞行时间等优化目标,建立了复杂终端区进场交通流优化排序模型,并采用带精英策略的非支配排序遗传算法对所建模型进行求解。选取上海多机场组成的复杂终端区进行实例验证,仿真实验表明提出的优化方法相比先到先服务方法(First come first serve,FCFS),航班总延误时间减少20.7%,终端区等待时间减少60.7%,终端区进场交通流运行效率得到显著提升。展开更多
In this paper, a constrained genetic algorithm (CGA) is proposed to solve the single machine total weighted tardiness problem. The proposed CGA incorporates dominance rules for the problem under consideration into the...In this paper, a constrained genetic algorithm (CGA) is proposed to solve the single machine total weighted tardiness problem. The proposed CGA incorporates dominance rules for the problem under consideration into the GA operators. This incorporation should enable the proposed CGA to obtain close to optimal solutions with much less deviation and much less computational effort than the conventional GA (UGA). Several experiments were performed to compare the quality of solutions obtained by the three versions of both the CGA and the UGA with the results obtained by a dynamic programming approach. The computational results showed that the CGA was better than the UGA in both quality of solutions obtained and the CPU time needed to obtain the close to optimal solutions.The three versions of the CGA reduced the percentage deviation by 15.6%, 61.95%, and 25% respectively and obtained close to optimal solutions with 59% lower CPU time than what the three versions of the UGA demanded. The CGA performed better than the UGA in terms of quality of solutions and computational effort when the population size and the number of generations are smaller.展开更多
文摘为提高终端区时空资源利用率,增强空中交通运行效率,研究了复杂终端区进场交通流优化排序问题。通过深入剖析终端区进场定位点、航路航线、多跑道系统等资源运行特性,综合考虑尾流间隔、移交间隔、多跑道运行间隔等各类约束限制,以及最小化航班延误时间、最大化跑道运行容量、最小化终端区飞行时间等优化目标,建立了复杂终端区进场交通流优化排序模型,并采用带精英策略的非支配排序遗传算法对所建模型进行求解。选取上海多机场组成的复杂终端区进行实例验证,仿真实验表明提出的优化方法相比先到先服务方法(First come first serve,FCFS),航班总延误时间减少20.7%,终端区等待时间减少60.7%,终端区进场交通流运行效率得到显著提升。
文摘In this paper, a constrained genetic algorithm (CGA) is proposed to solve the single machine total weighted tardiness problem. The proposed CGA incorporates dominance rules for the problem under consideration into the GA operators. This incorporation should enable the proposed CGA to obtain close to optimal solutions with much less deviation and much less computational effort than the conventional GA (UGA). Several experiments were performed to compare the quality of solutions obtained by the three versions of both the CGA and the UGA with the results obtained by a dynamic programming approach. The computational results showed that the CGA was better than the UGA in both quality of solutions obtained and the CPU time needed to obtain the close to optimal solutions.The three versions of the CGA reduced the percentage deviation by 15.6%, 61.95%, and 25% respectively and obtained close to optimal solutions with 59% lower CPU time than what the three versions of the UGA demanded. The CGA performed better than the UGA in terms of quality of solutions and computational effort when the population size and the number of generations are smaller.